Content area
Full text
ABSTRACT
Existing techniques of quality control of radar reflectivity data rely on local texture and vertical profiles to discriminate between precipitating echoes and nonprecipitating echoes. Nonprecipitating echoes may be due to artifacts such as anomalous propagation, ground clutter, electronic interference, sun strobe, and biological contaminants (i.e., birds, bats, and insects). The local texture of reflectivity fields suffices to remove most artifacts, except for biological echoes. Biological echoes, also called "bloom" echoes because of their circular shape and expanding size during the nighttime, have proven difficult to remove, especially in peak migration seasons of various biological species, because they can have local and vertical characteristics that are similar to those of stratiform rain or snow. In this paper, a technique is described that identifies candidate bloom echoes based on the range variance of reflectivity in areas of bloom and uses the global, rather than local, characteristic of the echo to discriminate between bloom and rain. Every range gate is assigned a probability that it corresponds to bloom using morphological (shape based) operations, and a neural network is trained using this probability as one of the input features. It is demonstrated that this technique is capable of identifying and removing echoes due to biological targets and other types of artifacts while retaining echoes that correspond to precipitation.
1. Introduction
Weather radar data are used operationally to warn of impending severe weather (Kitzmiller et al. 1995) and to create high-resolution precipitation estimates (Fulton et al. 1998). Radar data are routinely assimilated into numerical weather models and used for the prediction of convective systems (Sun and Wilson 2003). Simmons and Sutter (2005) demonstrated that expected fatalities due to tornadoes after Doppler radar installation in the United States were 45% lower and expected injuries were 40% lower.
All of these uses of weather radar require that radar echoes correspond, broadly, to precipitation. By removing ground-clutter contamination, rainfall from the radar data using the National Weather Service Weather Surveillance Radar-1988 Doppler (WSR-88D) can be improved (Fulton et al. 1998; Krajewski and Vignai 2001). A large number of false positives for the mesocyclone detection algorithm (Stumpf et al. 1998) are caused in regions of clear-air return (McGrath et al. 2002; Mazur et al. 2004). A hierarchical motion-estimation technique segments and forecasts...